## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1027317 54.9 2127068 113.6 1296870 69.3
## Vcells 1721619 13.2 8388608 64.0 2221721 17.0
Definitions
|
Ciudad
|
Trip
|
Collection
|
Diary
|
Frequency
|
Trip_duration
|
|
Bogota2019
|
Moving from one part to another with a specific reason/motive, a definite hour of start and end, a mode of transport, and a duration greater than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration
|
Trips made the day of reference, i.e., the day before the survey (from 4am yesterday to 4am today). Surveys made from Monday to Sunday (according to the dataset).
|
No diary, questionnaire
|
Yes. Monday to Sunday or Occasional
|
Already in the dataset
|
|
Mexico
|
Moving from one part to another with a specific reason/motive, using one or multiples modes of transport
|
Trips made during the week (Tuesday, Wednesday, Thursday) and in Saturdays (weekends)
|
Yes, one for weekdays and one for saturdays
|
No
|
I calculated it
|
|
Cali
|
Moving from one part to another with a specific reason/motive and a duration longer than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration
|
Trips made the day of reference, i.e., the day before the survey (from 4am yesterday to 4am today). Surveys made from Monday to Sunday (according to the dataset).
|
No diary, questionnaire
|
No
|
Already in the dataset
|
|
Medellin
|
Couldn’t find the definition
|
Trips made the day of reference, i.e., last 24 hours
|
No diary, questionnaire
|
Yes, daily, weekly, monthly, yearly.
|
I calculated it
|
|
Santiago
|
Any movement carried out on public roads with a purpose determined, between two places (origin and destination) at a certain time of day; It can be carried out in several modes of transport and consist of one or more stages
|
Trips made in working days (regular season), in weekends (regular season) and in working days(summer season)
|
Yes, a day was randomly assigned to each respondent.
|
No
|
Already in the dataset
|
|
Sao Paulo
|
Moving for a specific reason between two specific points (origin and destination), using one or more modes of transport. Walking trips where the reason for the trip is work or school, regardless of the distance travelled; or the distance covered is more than 500 mts for other reasons.
|
Trips made the day before the survey (from 4am yesterday to 3:59am today).
|
No diary, questionnaire
|
No, but it asks the day of the week when the trip was made
|
Already in the dataset
|
|
Rosario
|
A trip of 4 blocks or more (from the questionnaire).
|
Trips made the working day before the survey (from 4am yesterday to 4am today).
|
No diary, questionnaire
|
No
|
Already in the dataset
|
|
Lima
|
only people over 6 years old.
|
…
|
No diary, questionnaire
|
No
|
I have to calculate it
|
|
Montevideo
|
…
|
Trips made the day before the survey (from 4am yesterday to 4am today).
|
No diary, questionnaire
|
Yes, 5 days a week, 3-4 days a week, 1-2 days a week, 2-3 days a month, once a month
|
I have to calculate it
|
Summary table
|
|
Bogota2015
|
Bogota2019
|
Bogota2019_longer15
|
Mexico
|
Mexico_weekdays
|
Mexico_weekends
|
Medellin
|
Cali
|
Santiago
|
|
Min.
|
0.0
|
0.0
|
0.0
|
0.0
|
1
|
1.0
|
1.0
|
0.0
|
0.0
|
|
1st Qu.
|
14.0
|
15.0
|
25.0
|
20.0
|
15
|
15.0
|
15.0
|
10.0
|
15.0
|
|
Median
|
32.2
|
30.0
|
45.0
|
43.0
|
30
|
30.0
|
30.0
|
25.0
|
30.0
|
|
Mean
|
39.7
|
50.6
|
58.6
|
52.4
|
43
|
43.2
|
33.7
|
42.2
|
36.9
|
|
3rd Qu.
|
61.6
|
60.0
|
75.0
|
75.0
|
60
|
60.0
|
45.0
|
45.0
|
50.0
|
|
Max.
|
553.7
|
1110.0
|
1110.0
|
1200.0
|
840
|
735.0
|
600.0
|
1282.0
|
1335.0
|
|
NA’s
|
22515.0
|
10319.0
|
13899.0
|
0.0
|
17964
|
37916.0
|
6494.0
|
12618.0
|
14066.0
|
Density plot
These plots are interactive so we can zoom in and out, and select cities.
g1 <- ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago)
)
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: Removed 10319 rows containing non-finite values (stat_density).
## Warning: Removed 13899 rows containing non-finite values (stat_density).
## Warning: Removed 17964 rows containing non-finite values (stat_density).
## Warning: Removed 37916 rows containing non-finite values (stat_density).
## Warning: Removed 6494 rows containing non-finite values (stat_density).
## Warning: Removed 12618 rows containing non-finite values (stat_density).
## Warning: Removed 14066 rows containing non-finite values (stat_density).
#htmlwidgets::saveWidget(g1, "g1.html")
#display_html('')
Density plot by mode
Bogota 2015
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2015))
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Bogota 2019
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2019))
## Warning: Removed 10319 rows containing non-finite values (stat_density).
Bogota 2019 walking trips longer than 15 minutes
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = bogota_2019_longer15))
## Warning: Removed 13899 rows containing non-finite values (stat_density).
Mexico
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico))
Mexico weekdays
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico_weekdays))
## Warning: Removed 17964 rows containing non-finite values (stat_density).
Mexico weekends
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = mexico_weekends))
## Warning: Removed 37916 rows containing non-finite values (stat_density).
Medellin
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = medellin))
## Warning: Removed 6494 rows containing non-finite values (stat_density).
Cali
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = cali))
## Warning: Removed 12618 rows containing non-finite values (stat_density).
Santiago
ggplotly(ggplot() +
geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode),
alpha = .3 , data = santiago))
## Warning: Removed 14066 rows containing non-finite values (stat_density).
Comparison of walking trips
ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15 %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali %>%
filter(trip_mode == "walk")) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago %>%
filter(trip_mode == "walk"))
)
## Warning: Removed 4 rows containing non-finite values (stat_density).
## Warning: Removed 1 rows containing non-finite values (stat_density).
## Warning: Removed 398 rows containing non-finite values (stat_density).
## Warning: Removed 2 rows containing non-finite values (stat_density).
Comparison of cycling trips
ggplotly(
ggplot() +
geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 ,
data = bogota_2015 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 ,
data = bogota_2019 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 ,
data = bogota_2019_longer15 %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 ,
data = mexico %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 ,
data = mexico_weekdays %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 ,
data = mexico_weekends %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 ,
data = medellin %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 ,
data = cali %>%
filter(trip_mode == "bicycle")) +
geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 ,
data = santiago %>%
filter(trip_mode == "bicycle"))
)
## Warning: Removed 40 rows containing non-finite values (stat_density).